This gist has USWDS themes for two components. The CSS for these themes are in the other files in this gist.
Documentation for these components:
This gist has USWDS themes for two components. The CSS for these themes are in the other files in this gist.
Documentation for these components:
Use the SSH keys stored in your Windows Bitwarden Desktop vault from inside
WSL2 — ssh, git, ssh-add, etc. — managed by a systemd user service
instead of a fragile .zshrc snippet.
This bridges the Windows OpenSSH-agent named pipe that Bitwarden exposes
(\\.\pipe\openssh-ssh-agent) to a Unix socket inside WSL using
npiperelay + socat.
I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.
So below I made a list of leetcode problems that are as close to grokking problems as possible.
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
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Smooth-Flow is my nitpicky Luau coding style that encases various patterns that I've observed with how I've gone about writing and cleaning up Luau code over the past few years.
It is 99% compatible with StyLua and Roblox's Lua style guide, it just introduces some additional nitpicking and aesthetic changes to help with the readability and overall presentability of the Luau code.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Team Wiki — a specification for a centralized, agent-maintained wiki on S3 + a Lambda MCP server. Hand it to an agent to get it set up.
A shared knowledge base your agents write and maintain themselves — plain markdown in S3, reached through an MCP server.
Features